Uncertainty-aware Automated Liver Macromolecular Proton Fraction Quantification

Motivation: Macromolecular Proton Fraction quantification based on spin-lock MRI (MPF-SL) is a new technique for non-invasive imaging and characterization of macromolecule environment in tissues. Goal(s): This study aims to develop an automated method for MPF quantification in the liver. Approach: We present a deep learning framework for automated liver MPF quantification, incorporating an uncertainty-guided strategy for reliable region-of-interest (ROI) selection. Results: Evaluation was conducted using clinical MPF data from 44 patients, demonstrating minimal error in MPF quantification and consistent and robust ROI selection. Our method shows promise in automated MPF measurement of the liver, offering both qualitative and quantitative evidence of its efficacy. Impact: MPF-SL has been recently developed to measure macromolecule levels, showing potential in the non-invasive diagnosis of hepatic fibrosis. This work automates MPF quantification using deep learning, showing the potential to decrease the cost of MPF-SL post-processing.